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base_curve_analysis.py
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base_curve_analysis.py
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#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pl
import pandas as pd
import seaborn as sns
'''This script will load data from a csv file and fit a curve to the base material being tested'''
def loadfile(filename):
data = np.loadtxt(filename, delimiter=',')
data = data[1:-1, :]
return data
def split_data(data, displacement_level):
# returns an array for that target displacement level
# input: data array Nx16
# target displacement to extract from data array
# output: list of data arrays for that discplament level. The length of the list is the number of trials at that target displacement.
idx = np.where(data[:, 0] == displacement_level) # find the entries in the data that equal the target displacement
split_disp = data[idx] # grab only those entries
max_trials = np.amax(split_disp[:,1]) # look at the trial number to get the max trial number
idxes =[]
output=[]
for i in range(0,int(max_trials)): # for each trial do
idxes.append(np.where(split_disp[:, 1] == i+1)) # append to list an array of idxes corresponding to that trial
output.append(split_disp[idxes[i]]) # append to list an array of the outputs of that trial.
output[i][:,6] = output[i][:,6]-output[i][0,6] # zero out column 6 using the initial displacement
return output
def interpAndAvg(data):
# input: data as a list of data arrays corresponding to the same forces.
# output: an vector of the displacement and a vector of force
# This function first splits each array (in the list) into an elongation and retraction portion and interpolates the curves using the first curves displacement values
# Once they are interpolated into the same x values, it averages them to get a single curve.
numData = len(data)
new_data = []
# split the array into elongation and relaxation portions
for i in range(0, numData):
idx_max = np.argmax(data[i][:, 13])
new_data.append(data[i][0:idx_max, :])
new_data.append(data[i][idx_max:-1, :])
numData = len(new_data)
scrubbed_data = []
interped_forces = []
interped_displacements = []
for i in range(0, numData):
# first determine that the reference array has unique values of force and time
scrubbed_data.append(check_unique(new_data[i]))
if i > 0:
interped_forces.append(np.interp(scrubbed_data[0][:, 6], scrubbed_data[i][:, 6], scrubbed_data[i][:, 13]))
interped_displacements.append(scrubbed_data[i][:, 6])
mean_force_arr = np.vstack((mean_force_arr, np.array(interped_forces[i])))
# mean_disp_arr = np.vstack((mean_disp_arr, np.array(interped_displacements[i])))
else:
interped_forces.append(scrubbed_data[i][:, 13]) # initialize the force data points
interped_displacements.append(scrubbed_data[i][:, 6]) # initialize the displacement data points
mean_force_arr = np.array(interped_forces[i])
# mean_disp_arr = np.array(interped_displacements[i])
mean_force = np.mean(mean_force_arr, 0)
mean_disp = scrubbed_data[0][:, 6]
# mean_disp = np.mean(mean_disp_arr,0)
return mean_disp, mean_force
def check_unique(data):
# takes in an array
val, idxF = np.unique(data[:, 13], return_index=True)
scrubbed_data = data[idxF]
val, idxD = np.unique(scrubbed_data[:, 6], return_index=True)
unique_data = scrubbed_data[idxD]
return unique_data
def plot_raw(segmented_data,color):
list_size = len(segmented_data)
#plt.figure()
for i in range(0, list_size):
numTrials = len(segmented_data[i])
for j in range(0, numTrials):
force = segmented_data[i][j][:, 13]
displacement = -segmented_data[i][j][:, 6]
plt.plot(displacement, force,color=color)
#plt.plot(displacement, force)
#plt.show()
def plot_avgs(displacement_list, force_list):
numForces = len(force_list)
color = sns.color_palette("GnBu_d",numForces)
#plt.figure()
for j in range(0, numForces):
force = force_list[j]
displacement = displacement_list[j]
plt.plot(-displacement, force, color = color[j])
#plt.show()
def plot_avg_seaborn(displacement_list,force_list):
# for this functiont the displacements in discplacement list should be all the same.
sns.set_palette("Reds_r")
df = pd.DataFrame()
disp = []
force = []
name = []
for i in range(len(displacement_list)):
disp = disp+ list(-1*displacement_list[i])
force = force + list(force_list[i])
name = name + [str(i) for k in range(len(displacement_list[i]))]
df['disp'] = disp
df['force'] = force
df['name'] = name
#plt.figure()
g = sns.lineplot(x="disp", y="force",ci = 'sd' ,data = df )
return df,g
def fit_poly3(displacement_list, force_list):
# takes in a list of forces and their corresponding displacements
# returns a 1-D polynomial class
numForces = len(force_list)
# iterate through the force and disp list and populate a 2 arrays of data point pairs
for i in range(0, numForces):
if i == 0:
aggregate_forces = force_list[i]
aggregate_disp = displacement_list[i]
else:
aggregate_forces = np.hstack((aggregate_forces, force_list[i]))
aggregate_disp = np.hstack((aggregate_disp, displacement_list[i]))
coeffs = np.polyfit(-aggregate_disp, aggregate_forces, 3)
p = np.poly1d(coeffs)
return p
def plot_poly3(polyfunc, displacement_list, force_list,color='r'):
# taks in 1-D polynomial object and the list of average displacments and forces
numForces = len(force_list)
# iterate through the force and disp list and populate a 2 arrays of data point pairs
for i in range(0, numForces):
if i == 0:
aggregate_forces = force_list[i]
aggregate_disp = displacement_list[i]
else:
aggregate_forces = np.hstack((aggregate_forces, force_list[i]))
aggregate_disp = np.hstack((aggregate_disp, displacement_list[i]))
x_poly = -displacement_list[-1]
#plt.figure()
#plt.plot(-aggregate_disp, aggregate_forces, '.b')
plt.plot(x_poly, polyfunc(x_poly),color = color)
#plt.show()
def get_force_error_bounds(polyfunc, target_forces, eps):
# takes in 1-D polynomial object and a list of target forces from which to calculate an error bounds based on
# eps value which is given in terms of displacement.
force_bound = np.zeros((3,len(target_forces)))
for i in range(0,len(target_forces)):
roots = (polyfunc - target_forces[i]).r
rv = roots.real[abs(roots.imag) < 1e-5]
force_bound[0,i] = target_forces[i]
force_bound[1,i] = np.absolute(polyfunc(rv + eps))
force_bound[2,i] = np.absolute(polyfunc(rv-eps))
print('target force = ' + str(target_forces[i]))
print('upper bound = ' + str(np.absolute(force_bound[1,i])))
print('lower bound = ' + str(np.absolute(force_bound[2,i])))
return force_bound
#%%
if __name__ == "__main__":
# START MAIN SCRIPT
filename = "base_curve"
p = []
#color = pl.cm.jet(np.linspace(0,1,15))
color = sns.color_palette("Blues",15)
fig, axs = plt.subplots(2)
k= 0
for j in range(0, 15):
#for j in [2,3]:
filename = "basecurves_062419/original names/062419_" + str(j + 1) + "_2.csv"
#filename = "basecurves_062419/original names/062419" + str(j + 1) + "_2.csv"
print(filename)
data = loadfile(filename)
target_displacements = np.unique(data[:, 0])
segmented_data = []
average_disp_curves = []
average_force_curves = []
# split the data and average the load and unload portions over all trials at that discplacement level to get a single curve
for i in range(0, np.shape(target_displacements)[0]):
segmented_data.append(split_data(data, target_displacements[i]))
average_disp, average_force = interpAndAvg(segmented_data[i])
average_disp_curves.append(average_disp)
average_force_curves.append(average_force)
# some visualization code right here
#plot_raw(segmented_data,color[j])
plt.sca(axs[0])
plot_avgs(average_disp_curves, average_force_curves)
p.append(fit_poly3(average_disp_curves, average_force_curves))
plt.sca(axs[1])
#plot_poly3(p[j], average_disp_curves, average_force_curves)
plot_poly3(p[k], average_disp_curves, average_force_curves)
k+=1
plt.draw()
#plt.waitforbuttonpress()
#plt.close("all")
for j in range(0, 14):
#for j in [2,3]:
filename = "basecurves_062619/original names/062619_" + str(j + 1) + "_2.csv"
#filename = "basecurves_062419/original names/062419" + str(j + 1) + "_2.csv"
print(filename)
data = loadfile(filename)
target_displacements = np.unique(data[:, 0])
segmented_data = []
average_disp_curves = []
average_force_curves = []
# split the data and average the load and unload portions over all trials at that discplacement level to get a single curve
for i in range(0, np.shape(target_displacements)[0]):
segmented_data.append(split_data(data, target_displacements[i]))
average_disp, average_force = interpAndAvg(segmented_data[i])
average_disp_curves.append(average_disp)
average_force_curves.append(average_force)
# some visualization code right here
#plot_raw(segmented_data,color[j])
plt.sca(axs[0])
plot_avgs(average_disp_curves, average_force_curves)
p.append(fit_poly3(average_disp_curves, average_force_curves))
plt.sca(axs[1])
#plot_poly3(p[j], average_disp_curves, average_force_curves)
plot_poly3(p[k], average_disp_curves, average_force_curves,'b')
k+=1
plt.draw()
#plt.waitforbuttonpress()
#plt.close("all")
plt.show()
#%% I think this code does an x-offset
plt.figure()
# curves we like is what will get
curves_we_like = [0] # always -1 of the sample number
#curves_we_like = np.arange(0,15)
f_curves_we_like = []
d_curves_we_like = []
ref_curve_idx = 0
# the x offset makes an assumption. that it is not the force sensor that has a offset error, but the length of the sample at zero that has some offset error?
# need to think more about this...
for i in curves_we_like:
# the x offset is the difference of the x intercepts of the reference curve and the curve i
# we get this by taking the largest root that is not complex for both curves.
x_offset = np.amax(np.roots(p[ref_curve_idx])[np.iscomplex(np.roots(p[ref_curve_idx]))==False])-np.amax(np.roots(p[i])[np.iscomplex(np.roots(p[i]))==False])
# using the offset and the displacement values of the last curve (why though?) we can recompute the forces but now shifted.
f_curves_we_like.append(p[i](-(average_disp_curves[-1]+x_offset)))
d_curves_we_like.append(average_disp_curves[-1])
p_avg = fit_poly3(d_curves_we_like,f_curves_we_like)
plot_poly3(p_avg,d_curves_we_like,f_curves_we_like)
#plot_avgs(d_curves_we_like,f_curves_we_like)
plot_avg_seaborn(d_curves_we_like,f_curves_we_like)
plt.show()
#%% I think this code does the y offset
color = pl.cm.jet(np.linspace(0,1,len(p)))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
for i in curves_we_like: #for i in range(0, len(p)):
#p[i].coeffs[5] += -p[ref_curve_idx](-average_disp_curves[-1])[-1] - p[i](-average_disp_curves[-1])[-1]
#x_offset = np.amax(np.roots(p[ref_curve_idx])[np.iscomplex(np.roots(p[ref_curve_idx]))==False])-np.amax(np.roots(p[i])[np.iscomplex(np.roots(p[i]))==False])
y_offset = np.polyval(p[ref_curve_idx],0)-np.polyval(p[i],0)
#x_offset = np.roots(p[ref_curve_idx])[np.iscomplex(np.roots(p[ref_curve_idx]))==False][1]-np.roots(p[i])[np.iscomplex(np.roots(p[i]))==False][1]
#plt.plot(-average_disp_curves[-1], p[i](-(average_disp_curves[-1]+x_offset)),color=color[i])
x = np.linspace(-60,0,100)
plt.plot(-x, p[i](-x)+y_offset,color=color[i])
#plt.plot(-average_disp_curves[-1], p[i](-(average_disp_curves[-1]))+y_offset,color=color[i])
#plt.xlim((0,0.045))
major_ticks = np.arange(0, 60, 1)#major_ticks = np.arange(0, 0.06, 0.001)
minor_ticks = np.arange(0, 60, 0.1)#minor_ticks = np.arange(0, 0.06, 0.0001)
ax.set_xticks(major_ticks)
ax.set_xticks(minor_ticks, minor=True)
# And a corresponding grid
ax.grid(which='both')
# Or if you want different settings for the grids:
ax.grid(which='minor', alpha=0.75)
ax.grid(which='major', alpha=0.75)
#plt.legend(["1", '2', '3', '4', '5', '6','7','8','9','10','11','12','13','14','15','16','17','18','19'], loc=0)
#plt.plot(-average_disp_curves[-1], p_avg(-(average_disp_curves[-1]+x_offset)),lw=5)
plt.show()
#%%
force_bounds = get_force_error_bounds(p_avg,[1.5,2.5,3.5,4.5,6],2)
np.savetxt('force_bounds.csv',force_bounds,'%.3f',delimiter=',')
coeffs = p_avg.coeffs
coeffs = coeffs.real
np.savetxt('avg_curves_coeffs.csv',coeffs,'%.3f',delimiter=',')
print('end')
'''
#%% This can be used as a check for the maximum discplacement we will get
# calculate the displacements at 10N
print("calc roots at 10N")
array10n = []
for i in range(0,19):
y_offset = np.polyval(p[ref_curve_idx],0)-np.polyval(p[i],0)
yy = (p[i]-10-y_offset).r
array10n.append(np.real(yy[~np.iscomplex(yy)])[0])
print(array10n)
plt.figure()
plt.plot(np.linspace(1,19,19),array10n,'x')
plt.grid(which="both")
plt.show
#%%
import pickle
f = open('test_d.dat','rb')
test_displacement = pickle.load(f)
f.close()
f = open('test_f.dat','rb')
test_force = pickle.load(f)
f.close()
f=open('train_d.dat','rb')
train_displacement = pickle.load(f)
f.close()
f = open('train_f.dat','rb')
train_force = pickle.load(f)
f.close()
plt.gca()
n = 5-1
plt.plot(train_displacement[n],train_force[n],'.m')
plt.plot(test_displacement[n],test_force[n],'.g')
'''